We review the state of open science and the perspectives on open data sharing within the automotive user research community. Openness and transparency are critical not only for judging the quality of empirical research, but also for accelerating scientific progress and promoting an inclusive scientific community. However, there is little documentation of these aspects within the automotive user research community. To address this, we report two studies that identify (1) community perspectives on motivators and barriers to data sharing, and (2) how openness and transparency have changed in papers published at AutomotiveUI over the past 5 years. We show that while open science is valued by the community and openness and transparency have improved, overall compliance is low. The most common barriers are legal constraints and confidentiality concerns. Although research published at AutomotiveUI relies more on quantitative methods than research published at CHI, openness and transparency are not as well established. Based on our findings, we provide suggestions for improving openness and transparency, arguing that the motivators for open science must outweigh the barriers. All supporting materials are freely available at: https://osf.io/zdpek/

Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants’ acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. Interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot’s evaluation and effectiveness in everyday situations.

The rapid advancement of autonomous vehicle (AV) technology has opened up new possibilities and challenges in the domain of human–agent interaction. As AVs become increasingly prevalent on our roads, it is crucial to understand how humans perceive and interact with these intelligent systems. This workshop aims to bring together researchers and practitioners to explore the perception of cars as social agents. We explore the shift in user perception and the implications for interactions between autonomous vehicles, human drivers, and vulnerable road users (pedestrians, cyclists, etc.). Additionally, we investigate the communication of goals and intentions between cars and humans, as well as issues related to mixed agency, stakeholder perspectives, in-vehicle avatars, and human–vehicle power dynamics. The workshop aims to uncover the benefits, risks, and design principles associated with this emerging paradigm.

Domestic service robots become increasingly prevalent and autonomous, which will make task priority conflicts more likely. The robot must be able to effectively and appropriately negotiate to gain priority if necessary. In previous human-robot interaction (HRI) studies, imitating human negotiation behavior was effective but long-term effects have not been studied. Filling this research gap, an interactive online study ($N=103$) with two sessions and six trials was conducted. In a conflict scenario, participants repeatedly interacted with a domestic service robot that applied three different conflict resolution strategies: appeal, command, diminution of request. The second manipulation was reinforcement (thanking) of compliance behavior (yes/no). This led to a 3×2×6 mixed-subject design. User acceptance, trust, user compliance to the robot, and self-reported compliance to a household member were assessed. The diminution of a request combined with positive reinforcement was the most effective strategy and perceived trustworthiness increased significantly over time. For this strategy only, self-reported compliance rates to the human and the robot were similar. Therefore, applying this strategy potentially seems to make a robot equally effective as a human requester. This paper contributes to the design of acceptable and effective robot conflict resolution strategies for long-term use.

Human-robot conflicts might occur in the future, for instance, if a robot requests a public resource (e.g., an elevator). It needs to be investigated how the robot's request can be designed acceptably and effectively regarding the robot type, modality, and politeness. In this interactive video-based online study (N = 390), a robot requested priority over an elevator either using a polite or an assertive conflict resolution strategy presented via speech or on the robot's display. The robot was either humanlike, zoomorphic, or mechanoid. The mechanoid robot achieved more compliance than the humanoid robot if it used a verbal command. When the humanoid robot displayed the command instead of using its voice, more participants granted the robot priority. This might indicate that politeness norms are triggered more by a humanoid design and that if a robot makes a command, the modality should match the robot type.

When service robots will be employed in private and public spaces, conflicts in human-robot interaction (HRI) might arise. To gain priority and continue its tasks, the robot would benefit from conflict resolution strategies (CRS) that are acceptable and effective. Previous studies have mainly investigated verbal or text-based CRS. As verbal interaction might not suit every application context or robot type, movement-based CRS were investigated. First, four possible implementations were pre-tested in an online study (N = 101). Then two CRS varying in modality (verbal vs. motoric) and assertiveness (submissive vs. dominant) were tested for acceptance and compliance in a lab study (N = 31) and compared for three robot types (humanoid, zoomorphic, and mechanoid). The verbal appeal was the most effective strategy to achieve user compliance. The motoric dominant strategy (moving back and forth) was perceived as most assertive and least polite if applied by the mechanoid cleaning robot but was not more effective than the verbal strategy. These studies provide insights into the influence of robot type on the acceptability and effectiveness of robot conflict resolution behavior depending on the modality.

Automated driving has the potential to reduce road fatalities. However, the public opinion to use automated driving can be described as skeptical. To increase the use of automated driving features, we investigate the persuasion principle of opt-out permission policies for enabling the automation, meaning automatically enabling the automation if users do not veto. In a driving simulator study (n = 19), participants drove on three different tracks (city, highway, rural). Three different interface concepts (opt-out, opt-in, control) were examined regarding their effects on automation use, trust, and acceptance. We found that an opt-out activation policy may increase automation usage for some participants. However, opt-out was perceived as more persuasive and more patronizing than the other conditions. Most importantly, opt-out can lead to mode confusion and therefore to dangerous situations. When such an opt-out policy is used in an automated vehicle, mode confusion must be addressed.

Simulator studies have been conducted in the automotive domain since the 1960s. Recently, automated driving studies have become more popular as real-world automated cars start to emerge but at this time not all levels of automation can be realized. A simulation does not entail all details of real driving, creating a realistic simulation experience - both on a psychological and physical level - proposes recurring challenges. These are among others: sample acquisition, simulator sickness, simulator training, interface design, take-over requests and secondary tasks in automated driving simulator studies. In this paper, we review existing literature and summarize important lessons from simulations in the domain of driving automation to provide considerations for studies investigating driver behavior in the age of highly automated driving.

Mobile virtual reality (VR) head-mounted displays (HMDs) allow users to experience highly immersive entertainment whilst being in a mobile scenario. Long commute times make casual gaming in public transports and cars a common occupation. However, VR HMDs can currently not be used in moving vehicles since the car's rotation affects the HMD's sensors and simulator sickness occurs when the visual and vestibular system are stimulated with incongruent information. We present CarVR, a solution to enable VR in moving vehicles by subtracting the car's rotation and mapping vehicular movements with the visual information. This allows the user to actually feel correct kinesthetic forces during the VR experience. In a user study (n = 21), we compared CarVR inside a moving vehicle with the baseline of using VR without vehicle movements. We show that the perceived kinesthetic forces caused by CarVR increase enjoyment and immersion significantly while simulator sickness is reduced compared to a stationary VR experience. Finally, we explore the design space of in-car VR entertainment applications using real kinesthetic forces and derive design considerations for practitioners.

Human errors are a major reason for traffic accidents. One of the aims of the introduction of automated driving functions in vehicles is to prevent such accidents as such systems are supposed to be more reliable, react faster with higher precision. Therefore, we assume that an increase of automation features will also increase safety. However, when drivers are not willing to relinquish control to the vehicle, safety benefits of automated vehicles do not take effect. Therefore, convincing drivers to actively make use of the automation when appropriate can increase traffic safety. In this paper we investigate the influence of system feedback in proactive, safety critical takeover situations in automated driving. In contrast to handover, which is initiated by the system, proactive takeover is initiated by the driver, who's intention for steering the car is the reason for driving manually. We compare auditory feedback with audio-visual feedback realized as a virtual co-driver in a user study. We conducted a virtual reality simulator study (n=38) to investigate how system feedback influences the willingness of drivers to relinquish control to the vehicle. There were three conditions of system feedback: in condition none no feedback was given, in condition audio spoken feedback was given, and in condition co-driver additionally to audio feedback, a virtual co-driver on the front passenger seat was displayed. Our research provides evidence that system feedback can lead to an increase of willingness to maintain automation and to follow its safety related advices.

Although there are already fully autonomous vehicles on the roads for testing purposes, a rollout is far away. Autonomous vehicles are still not able to handle everyday driving and remain reliant on the driver when they reach their system limitations. One suggested approach to this problem is handing over the control entirely to the driver, which might become annoying when such situations occur frequently. In contrast, we suggest the usage of cooperative interfaces to avoid full handovers in situations in which the system needs the driver, for instance to approve or monitor a specific maneuver. A driving simulator study with 32 participants revealed that they felt comfortable choosing how the system should handle a situation. They reportedly assessed the situations first instead of relying blindly on the system and were able to handle every situation safely. We report lessons learned regarding cooperative interaction and interfaces, and their in-lab evaluation.

Belt is a novel unobtrusive input device for wearable displays that incorporates a touch surface encircling the user's hip. The wide input space is leveraged for a horizontal spatial mapping of quickly accessible information and applications. We discuss social implications and interaction capabilities for unobtrusive touch input and present our hardware implementation and a set of applications that benefit from the quick access time. In a qualitative user study with 14 participants we found out that for short interactions (2-4 seconds), most of the surface area is considered as appropriate input space, while for longer interactions (up to 10 seconds), the front areas above the trouser pockets are preferred.